Historically, approaches to classification systems for impulsively activated underwater sonar have largely relied on exploiting signal waveform features to distinguish target echoes from clutter echoes. Typically, one uses signal waveform features as the input for various classification algorithms designed in a laboratory environment. Using data acquired and processed from prior training exercises, these classification algorithms are trained to increase their accuracy.
However, signal waveform features often exhibit environmental sensitivities, which, when unaccounted for, lead to degraded classification performance of sonar energy detections. These environmental sensitivities may occur as a function of factors such as, water temperature, time of year, target depth, and geometry of the ocean bottom. These factors significantly affect underwater acoustic propagation by creating clutter that can interfere with properly classifying signal data. Even if one could develop predictable target echo features based on physics, one would still find the statistical behavior of clutter with respect to detected features difficult, if not impossible, to predict for a particular environment. In the absence of accurate predictions, inaccurate signal classifications result.
In addition, when operating in new environments without historical data to guide signal classification, traditional approaches to signal classification often yield inaccurate results. Improved systems and methods of signal classification are therefore needed.